Jan Thomas Müller

That is my solution: ``` import multiprocessing import pyttsx3 import time from threading import Thread def threaded(fn): def wrapper(*args, **kwargs): thread = Thread(target=fn, args=args, kwargs=kwargs) thread.start() return thread return wrapper...

Weights are pruned in a structured manner that is considered "physical." In this context, "physical" pruning refers to the actual removal of tensor entries rather than the common approach of...

When setting up the device of a tensor, there are two primary methods: 1. **Explicit definition:** You can explicitly define the device for a tensor, specifying whether it should be...

`GLU` is currently not supported, so it's treated as an element-wise operation. However, since `split` is supported, you can create your own `GLU` operation like this: ```python class CustomGLU(nn.Module): def...

> Hi @janthmueller, thanks for the workaround, I tried that but the network comes back with only the last conv layer pruned. No dep group with first conv layer is...

I believe that on $f_4^-$, the in_channels of a convolutional layer are pruned, which corresponds to the depth of all filters that the convolutional layer holds. On the other hand,...

Wow, would have loved to see your commit a week earlier. Thanks! When I compared my rebuilt with the original implementation via netron and named parameters, I now also noticed...

As far as the detection part is concerned, the cause is probably a too small number of training examples that have these features. I believe that, for example, static individuals...

Regarding the subsets of a dataset, the track ids already exist in this format, or are created using the gen_label modules. Regarding multiple datasets, for example, within the JointDataset class...

> > Regarding the subsets of a dataset, the track ids already exist in this format, or are created using the gen_label modules. Regarding multiple datasets, for example, within the...